A Robust Frame of WSN Utilizing Localization Technique

Wireless sensor networks are becoming increasingly popular due to their low cost and wide applicability to support a large number of diverse application areas. Localization of sensor nodes is a fundamental requirement that makes the sensor data meaningful. A wireless sensor network (WSN) consist of spatially distributed autonomous devices using sensors to monitor cooperatively physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants at different locations. The development of wireless sensor networks was originally motivated by a military application like battlefield surveillance. Node localization is required to report the origin of events, assist group querying of sensors, routing and to answer questions on the network coverage. So one of the fundamental challenges in wireless sensor network is node localization. This paper discusses different approaches of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Keywords: Localization, Particle Swarm Optimization, Received Signal Strength, Angle of Arrival

Hybrid algorithm for locating mobile station in cellular network

Locating mobile stations have been attracting an increasing attention from both researchers and industry communities and it is one of the most popular research areas of cellular network.Locating mobile stations using Time of Arrival, Time Difference of Arrival, Angle of Arrival and Received Signal Strength techniques have been widely used.However, more accurate results have been achieved by combining two or more of these techniques.A hybrid algorithm for locating mobile station is proposed by combining Received Signal Strength, Signal Attenuation and Time Difference of Arrival in this paper

Клинические и генетико-молекулярные исследования смешанной болезни соединительной ткани

Catedra Medicină Internă nr.1, USMF „Nicolae Testemiţanu”, Institutul de Genetică şi Fiziologie a Plantelor al AŞMThe article presents research on two related topics: 1) connective tissue disease, manifested in the articular modification and in the condition of the joints themselves, and in clinical observation, and, 2) polymorphism in DNA fragments (PCR analyses) of the genes controlling synthesis cytokines - IL 1, IL 6 and CTLA 4. Clusterian analyses revealed the degree of relationship between joint and extrajoint clinical manifestation and the gene mutations from a sample of mixed connective tissue disease patients. These were contrasted with a control sample of patients suffering from i.e., systemic lupus erythematosus, systemic sclerosis and rheumatoid arthritis.В статье приводятся данные относительно клинических суставных и внесуставных проявлений при смешанной болезни соединительной ткани (СБСТ), а также полиморфизма фрагментов ДНК (ПЦР-анализ) генов, контролирующих синтез цитокинов ИЛ 1, ИЛ 6 и ЦТЛА-4. Кластерным анализом были выявлены специфические ассоциации клинических суставных и внесуставных проявлений с мутациями, изученных генов у больных СБСТ и контрольных групп – системная красная волчанка, системная склеродермия и ревматоидный артрит

Optimal stochastic linearization for range-based localization

In range-based localization, the trajectory of a mobile object is estimated based on noisy range measurements between the object and known landmarks. In order to deal with this uncertain information, a Bayesian state estimator is presented, which exploits optimal stochastic linearization. Compared to standard state estimators like the Extended or Unscented Kalman Filter, where a point-based Gaussian approximation is used, the proposed approach considers the entire Gaussian density for linearization. By employing the common assumption that the state and measurements are jointly Gaussian, the linearization can be calculated in closed form and thus analytic expressions for the range-based localization problem can be derived

Optimizing Techniques and Cramer-Rao Bound for Passive Source Location Estimation

This work is motivated by the problem of locating potential unstable areas in underground potash mines with better accuracy more consistently while introducing minimum extra computational load. It is important for both efficient mine design and safe mining activities, since these unstable areas may experience local, low-intensity earthquakes in the vicinity of an underground mine. The object of this thesis is to present localization algorithms that can deliver the most consistent and accurate estimation results for the application of interest. As the first step towards the goal, three most representative source localization algorithms given in the literature are studied and compared. A one-step energy based grid search (EGS) algorithm is selected to address the needs of the application of interest. The next step is the development of closed-form Cram´er-Rao bound (CRB) expressions. The mathematical derivation presented in this work deals with continuous signals using the Karhunen-Lo`eve (K-L) expansion, which makes the derivation applicable to non-stationary Gaussian noise problems. Explicit closed-form CRB expressions are presented only for stationary Gaussian noise cases using the spectrum representation of the signal and noise though. Using the CRB comparisons, two approaches are proposed to further improve the EGS algorithm. The first approach utilizes the corresponding analytic expression of the error estimation variance (EEV) given in [1] to derive an amplitude weight expression, optimal in terms of minimizing this EEV, for the case of additive Gaussian noise with a common spectrum interpretation across all the sensors. An alternate noniterative amplitude weighting scheme is proposed based on the optimal amplitude weight expression. It achieves the same performance with less calculation compared with the traditional iterative approach. The second approach tries to optimize the EGS algorithm in the frequency domain. An analytic frequency weighted EEV expression is derived using spectrum representation and the stochastic process theory. Based on this EEV expression, an integral equation is established and solved using the calculus of variations technique. The solution corresponds to a filter transfer function that is optimal in the sense that it minimizes this analytic frequency domain EEV. When various parts of the frequency domain EEV expression are ignored during the minimization procedure using Cauchy-Schwarz inequality, several different filter transfer functions result. All of them turn out to be well known classical filters that have been developed in the literature and used to deal with source localization problems. This demonstrates that in terms of minimizing the analytic EEV, they are all suboptimal, not optimal. Monte Carlo simulation is performed and shows that both amplitude and frequency weighting bring obvious improvement over the unweighted EGS estimator